1. Upload the "employment.csv" data set.

2. Build the plot to look at the relationship between the variables.

What will the dependent variable (outcome), what will be the independent variable (predictor)?

3. Perform linear regression analysis (fit a simple linear regression model between the variables).

Draw the best-fit regression line.

4. Check the main assumptions of the model, use the four main plots for checking:

Plot 1. Linearity of the data, independence of residuals

Plot 2. Normality of residuals using Q-Q plot

Plot 3. Constant variance of residuals

Plot 4. No influential outliers

5. Check the assumption "Normality of residuals" using histogram and normality tests; and "Zero
mean of residuals".

Don't forget to look at the Q-Q plot from the previous question.

6. Obtain parameters of the regression (the intercept, the slope of the line, the 95% confidence
intervals).

7. Obtain criteria for the model evaluation (Adjusted R-squared, AIC).

8. After checking all the assumptions, what conclusion can you make?

9.Repeat all the steps for the "bpa_age_data.csv" data set.

10.Repeat all the steps for the "employment_1.csv" data set.

11. Repeat all the steps for the "age.csv" data set.



Check list


                                         "employment.csv"

                                        "bpa_age_data.csv"

                                        "employment_1.csv"

                                             "age.csv"

                               Assumptions after Linear regression:

                     Plot 1: Linearity of the data, independence of residuals





                                  Plot 2: Normality of residuals

                                   +histogram + normality tests





                                      Zero mean of residuals





                              Plot 3: Constant variance of residuals





                                  Plot 4: No influential outliers





                                   Parameters of the regression:

                                          - intercept (α)

                                     - slope of the line (β1)

                                              -95% CI





                                Criteria for the model evaluation:

                                           -Adjusted R^2

                                               -AIC





                                      Conclusion and formula

                                          (if relevant):